1. Field of the Invention
This invention pertains generally to imaging, and more particularly to ultrasound imaging using a synthetic aperture ultrasound waveform tomography.
2. Description of Related Art
Breast cancer is the second-leading cause of cancer death among American women. The breast cancer mortality rate in the U.S. has been flat for many decades, and has decreased only about 20% since the 1990s. Early detection is the key to reducing breast cancer mortality. There is an urgent need to improve the efficacy of breast cancer screening. Ultrasound tomography is a promising, quantitative imaging modality for early detection and diagnosis of breast tumors.
Ultrasound waveform tomography is gaining popularity, but is computationally expensive, even for today's fastest computers. The computational cost increases linearly with the number of transmitting sources.
Synthetic-aperture ultrasound has great potential to significantly improve medical ultrasound imaging. In a synthetic aperture ultrasound system, ultrasound from each element of a transducer array propagates to the entire imaging domain, and all elements in the transducer array receive scattered signals.
Many conventional ultrasound systems record only 180° backscattered signals. Others are configured to receive only transmission data from the scanning arrays. Accordingly, these systems suffer from extensive computational costs, insufficient resolution, or both.
Waveform inversion can be implemented either in the time domain, or in the frequency domain. Because of the ill-posedness caused by the limited data coverage, multiple local-minimum solutions exist, and therefore, certain stabilization numerical techniques need to be incorporated within inversion process to obtain a global-minimum solution. In recent years, many approaches have been developed for this purpose. Regularization techniques can be employed to alleviate the instability of the original problem. Preconditioning approaches can also be used in waveform inversion to create a well-conditioned problem with lower dimensions. In addition, prior information about the model can be introduced to improve the convergence of waveform inversion.
In waveform inversion with regularization, reconstruction results depend dramatically on the strength of the regularization, which is controlled by the regularization parameter. If the regularization parameter is too large, the inversion results are over regularized, which usually leads to over-smoothed reconstructions; on the other hand, if the regularization parameter is smaller than necessary, the inversion results are under regularized, and the reconstructions tend to be degraded by image artifacts and noise. Therefore, an appropriate regularization parameter is essential for high-resolution tomographic reconstructions. The shapes, sizes and densities and tumors and breast tissue can vary significantly within a breast. The conventional approach to regularization uses a constant regularization parameter for the entire imaging domain. This approach inevitably yields over-regularization for certain regions/tumors, and under-regularization of other regions/tumors.